Abstract
This paper proposes a novel genetic algorithm (GA) approach that utilizes a multichromosome to solve the flexible job-shop scheduling problem (FJSP), which involves two kinds of decisions: machine selection and operation sequencing. Typically, the former is represented by a string of categorical values, whereas the latter forms a sequence of operations. Consequently, the chromosome of conventional GAs for solving FJSP consists of a categorical part and a sequential part. Since these two parts are different from each other, different kinds of genetic operators are required to solve the FJSP using conventional GAs. In contrast, this paper proposes a unified GA approach that enables the application of an identical crossover strategy in both the categorical and sequential parts. In order to implement the unified approach, the sequential part is evolved by applying a candidate order-based GA (COGA), which can use traditional crossover strategies such as one-point or two-point crossovers. Such crossover strategies can also be used to evolve the categorical part. Thus, we can handle the categorical and sequential parts in an identical manner if identical crossover points are used for both. In this study, the unified approach was used to extend the existing COGA to a unified COGA (u-COGA), which can be used to solve FJSPs. Numerical experiments reveal that the u-COGA is useful for solving FJSPs with complex structures.
Highlights
Production scheduling is one of the most important decision-making procedures on manufacturing shopfloors, as it helps to utilize resources efficiently and maintain competitiveness in manufacturing companies [1,2,3]
In order to overcome the limitations of the traditional approach, this paper proposes a novel unified genetic algorithm (GA) approach for the flexible job-shop scheduling problem (FJSP), which enables the application of an identical crossover operator to both the operation sequence (OS) and machine selection (MS) parts
Most existing GAs for solving the FJSP use different crossover operators to recombine the multichromosome, which consists of the OS and MS parts
Summary
Production scheduling is one of the most important decision-making procedures on manufacturing shopfloors, as it helps to utilize resources efficiently and maintain competitiveness in manufacturing companies [1,2,3]. The traditional approach can cause a loss of good combinations in chromosomes and unnecessary diversity in the population To this end, this paper poses two research questions: how can we reduce the number of genetic operators in the GA for the FJSP, which will have a significant impact on the complexity of the GA? The most distinguished feature of the COGA is that simple point-based crossover strategies, including one-point and two-point and uniform crossovers, can be implemented [14] This enables the application of an identical point-based crossover strategy with an identical crossover point to the OS and MS parts, which may help to maintain the combinations of good order and good machine in parent solutions.
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